xAd: ‘Location Is the New Cookie’

Today mobile network xAd introduced a new sort of data visualization and offline analytics tool that shows consumer movements in and around businesses in real time. Called Footprints, it captures and reports the movements of millions of anonymous users throughout the US throughout the day.

Several years ago Sense Networks (recently acquired by YP) was heat-mapping cities based on user movements. As far as I know that effort was discontinued. Skyhook Wireless was also briefly beta-testing something similar. However, xAd’s Footprints is the first formal offline analytics and data visualization tool to be “commercialized” and operate at national scale.

Here’s how xAd describes Footprints (location is the new cookie):

As consumers go about their day traveling from one place to another, they engage with their phones regularly. Through this engagement, they often share their device location information with their favorite sites and applications. This data exchange normally occurs in an effort to make their mobile experiences more efficient or relevant to what they may be doing at any given time or place. It is through this anonymized location data that a product like Footprints™ is possible, essentially turning a device’s location data into a new kind of digital cookie.

The company also says, “Activity can be viewed by category or specific brand, and as broadly as nationally or as granular as by neighborhood.” The idea is to learn where are your customers . . . and what are they doing . . . when.

I haven’t yet seen what the screens look like, but the data are drawn collectively from anonymous users as they move through the world throughout the day. My sense is that the technology and data visualization are an extension of xAd’s Smart Fences dynamic geofencing technology.

Footprints is very interesting and another example of location being used for analytics and audience discovery purposes — not simply for geotargeted mobile advertising.

Very interesting – here at Placemeter we operate a bit in the same space although instead of relying on cellphones locations, we leverage computer vision on top of the millions of existing video streams. But very complementary approach – we have accuracy to the meter but not necessarily global coverage. With a good geo spatial inference model, combining the 2 datasets could be very powerful for retailers, commercial real estate, cities and transportation experts.